Tag
#MIT Schwarzman College of Computing
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MIT tool visualizes and edits “physically impossible” objects
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New algorithms enable efficient machine learning with symmetric data
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Robot, know thyself: New vision-based system teaches machines to understand their bodies
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A new way to edit or generate images
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The unique, mathematical shortcuts language models use to predict dynamic scenarios
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Can AI really code? Study maps the roadblocks to autonomous software engineering
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How to more efficiently study complex treatment interactions
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AI shapes autonomous underwater “gliders”
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Study could lead to LLMs that are better at complex reasoning
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Using generative AI to help robots jump higher and land safely
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LLMs factor in unrelated information when recommending medical treatments
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Unpacking the bias of large language models
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Photonic processor could streamline 6G wireless signal processing
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Inroads to personalized AI trip planning
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AI-enabled control system helps autonomous drones stay on target in uncertain environments
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Teaching AI models what they don’t know
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Teaching AI models the broad strokes to sketch more like humans do
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An anomaly detection framework anyone can use
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Building networks of data science talent
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AI learns how vision and sound are connected, without human intervention
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Learning how to predict rare kinds of failures
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With AI, researchers predict the location of virtually any protein within a human cell
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Study shows vision-language models can’t handle queries with negation words
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Hybrid AI model crafts smooth, high-quality videos in seconds
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New tool evaluates progress in reinforcement learning
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Novel AI model inspired by neural dynamics from the brain
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Making AI models more trustworthy for high-stakes settings
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The MIT-Portugal Program enters Phase 4
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Merging design and computer science in creative ways
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Designing a new way to optimize complex coordinated systems
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“Periodic table of machine learning” could fuel AI discovery
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3D modeling you can feel
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MIT’s McGovern Institute is shaping brain science and improving human lives on a global scale
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Making AI-generated code more accurate in any language
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A faster way to solve complex planning problems
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Training LLMs to self-detoxify their language
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New method efficiently safeguards sensitive AI training data
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Could LLMs help design our next medicines and materials?
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New method assesses and improves the reliability of radiologists’ diagnostic reports
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Researchers teach LLMs to solve complex planning challenges
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For this computer scientist, MIT Open Learning was the start of a life-changing journey
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MIT Maritime Consortium sets sail
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AI tool generates high-quality images faster than state-of-the-art approaches
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Robotic helper making mistakes? Just nudge it in the right direction
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Markus Buehler receives 2025 Washington Award
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Like human brains, large language models reason about diverse data in a general way
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AI model deciphers the code in proteins that tells them where to go
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Gift from Sebastian Man ’79, SM ’80 supports MIT Stephen A. Schwarzman College of Computing building
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Can deep learning transform heart failure prevention?
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Creating a common language
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Validation technique could help scientists make more accurate forecasts
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User-friendly system can help developers build more efficient simulations and AI models
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3 Questions: Modeling adversarial intelligence to exploit AI’s security vulnerabilities
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Expanding robot perception
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Toward video generative models of the molecular world
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Explained: Generative AI’s environmental impact
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Teaching AI to communicate sounds like humans do
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Ecologists find computer vision models’ blind spots in retrieving wildlife images
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MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures
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Lara Ozkan named 2025 Marshall Scholar
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Teaching a robot its limits, to complete open-ended tasks safely
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AI in health should be regulated, but don’t forget about the algorithms, researchers say